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JRM Vol.37 No.2 pp. 348-355
doi: 10.20965/jrm.2025.p0348
(2025)

Paper:

Multi-End Effector Selection by Depth-Aware Convolution

Yukiyasu Domae ORCID Icon

National Institute of Advanced Industrial Science and Technology
2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan

Received:
September 20, 2024
Accepted:
January 16, 2025
Published:
April 20, 2025
Keywords:
multi-end effectors, graspability, industrial robot
Abstract

In this paper, we propose a method for selecting end-effectors based on depth images for a robot that performs picking tasks using multiple end-effectors. The proposed method evaluates the graspability of each end-effector in a scene by convolving a hand model, represented as a two-dimensional binary structure, with the depth image of the target scene. A key feature of the method is that it requires no pre-training and does not rely on object or environmental models, operating solely with simple models of the end-effectors. In picking experiments involving eight types of electronic components commonly used in factory automation, the proposed method effectively alternated between suction and two-finger grippers. Compared to other training-free end-effector selection methods and approaches using a single end-effector, the proposed method demonstrated an improvement of over 14% in grasp success rate compared to the second-best method.

A diagram showing the selection results of two types of end effectors

A diagram showing the selection results of two types of end effectors

Cite this article as:
Y. Domae, “Multi-End Effector Selection by Depth-Aware Convolution,” J. Robot. Mechatron., Vol.37 No.2, pp. 348-355, 2025.
Data files:
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Last updated on Apr. 24, 2025